Age, ethnicity, life events and wellbeing among New Zealand women
Bibliographic record
Abstract
Abstract By the year 2030, 19–21 per cent of the population of New Zealand (NZ) is projected to be aged 65 and over. Like many countries, life expectancy in NZ differs by gender but also ethnicity: in 2019, life expectancy for Māori (indigenous) women was 77.1 years compared with 84.4 years for non-Māori women. If Māori and NZ European women are to flourish in later life, examining the factors associated with their wellbeing is paramount. The current study draws on the Life Course Perspective to explore how wellbeing is associated with age-related life events among mid- to later-life NZ women. The women in this study (N = 19,624) are participants in the 2018 wave of the New Zealand Attitudes and Values Study, a national probabilistic 20-year longitudinal study (mean age = 55.62; Māori = 10.8%, NZ European = 89.2%). We found that stressful life events were negatively associated with life satisfaction but positively associated with meaning in life. Māori women exhibited lower levels of life satisfaction but there were no ethnic differences for meaning in life; however, Māori and NZ European women showed different patterns of significant correlates associated with meaning in life. Findings highlight the necessity of an intersectional approach to the study of mid- to later-life wellbeing and the utility of measuring wellbeing in more than one way within NZ's unique cultural-historical context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".